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  DOI Prefix   10.20431


 

International Journal of Advanced Research in Chemical Science
Volume 4, Issue 11, 2017, Page No: 32-60

Multiple Linear Regression: An Overview with Analytical and Physico-Chemical Applications

Julia Martin1,Ana Maria Jimenez2,Maria Jose Navas3,M.A.Fernandez-Recamales4,Agustin G. Asuero5

1.Department of Analytical Chemistry, Escuela Politecnica Superior, The University of Seville, 41011-Seville(Spain).
2.Department of Analytical Chemistry, Faculty of Pharmacy, The University of Seville, 41012-Seville (Spain).
3.Department of Chemistry and Materials Science, Faculty of Experimental Sciences, University of Huelva,Campus de El Carmen, Huelva 21007 (Spain).

Citation :Julia Martin,et.al, Multiple Linear Regression: An Overview with Analytical and Physico-Chemical Applications International Journal of Advanced Research in Chemical Science 2017,4(11) : 32-60

Abstract

An overview on multiple linear regression (MLR) is envisaged in this paper. All but the final section is devoted to a discussion of the basic concepts of MLR. The corresponding MLR equations are derived and presented in a useful form for computing. However, the entirely general matrix approach to least squares applicable to any linear regression situation is also envisaged. In the final section selected analytical and physicochemical applications are shown in tabular form. MLR is one of the most widely used statistical tool and found applications on a number of areas such as quantitative structure property relationships (QSPR), quantitative structure retention relationships (QSRR), quantitative structure-transformation relationships (QSTR), molecular linear free energy relationships (MLFER) and quantitative structure activity relationships (QSAR), solvent polarity and solvatochromic effects, parameter estimation methods, correction of spectral (matrix) interferences, prediction, modelling and optimization, Fourier transform near infrared spectroscopy (NIR-FT) and multicomponent spectrophotometric analysis, and in many other areas.


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